Social payment for medical bills and bill settlement

Abstract

The disclosure provided herein is directed to a payment system configured to aggregate one or more bills related to one or more items in an online shopping environment or for one or more services or products already provided to a consumer or expected to be provided to a consumer in a single invoice or super-invoice, which in some instances is reviewed for consistency, errors, and gaps in service or products. Based on the super-invoice, the payment system is configured to counter the amount owed as well as collect payment for one or more benefactors through a variety of social media and/or networks to satisfy all or some of the amount owed. To facilitate the sufficiency of payment, the payment system is further configured to determine a consumer affordability score that corresponds to a consumer's capability to pay an amount owed.

What is claimed is: 
     
         1 . A system for bill settlement comprising:
 a payment system configured to receive a plurality of invoices and extract data from the plurality of invoices;   wherein the payment system is configured to provide a super-invoice that includes the data from the plurality of invoices and an aggregate amount owed;   wherein the payment system is configured to create a funding campaign;   the payment system having a distribution mechanism that is configured to transmit the funding campaign to a social network;   wherein the payment system is configured to receive a commitment by a benefactor to provide a payment to the super-invoice;   wherein the payment system is configured to determine whether the commitment is sufficient to pay the aggregate amount owed of the super-invoice;   the payment system having a payment mechanism to collect a payment from the benefactor based on the commitment; and   the payment system having a formula to allocate the payment to the plurality of invoices directly from the payment system.   
     
     
         2 . The system of  claim 1  further comprising the payment system having a trained model to identify at least one error, wherein the at least one error is selected from a group consisting of a billing error, a billing abuse, and a billing gap in the data from the plurality of invoices. 
     
     
         3 . The system of  claim 2  wherein the payment system is configured to provide an offer of payment to settle a portion of the super-invoice based on the error identified by the trained model. 
     
     
         4 . The system of  claim 1  wherein the payment system is configured to verify that the plurality of invoices belong to a single episode. 
     
     
         5 . The system of  claim 1  further comprising the payment system having a feedback mechanism configured to receive a quality rating for a service provider and provide quality ratings of other service providers. 
     
     
         6 . The system of  claim 1  further comprising the payment system having a feedback mechanism configured to receive a quality rating for a product and provide quality ratings of other products. 
     
     
         7 . The system of  claim 1  wherein the payment system is configured to compute and assign an affordability score to a consumer. 
     
     
         8 . The system of  claim 7  wherein the payment system is configured to provide an offer of payment to settle a portion of the super-invoice based on the affordability score of the consumer. 
     
     
         9 . The system of  claim 1  wherein the plurality of invoices are medical invoices. 
     
     
         10 . The system of  claim 9  wherein the plurality of invoices include medical services that have not been rendered. 
     
     
         11 . The system of  claim 1  wherein the plurality of invoices include medical products that have not been provided. 
     
     
         12 . The system of  claim 1  wherein the plurality of invoices are online retailer invoices for completing an online order. 
     
     
         13 . The system of  claim 1  wherein the payment system is configured to extract the data using optical character recognition. 
     
     
         14 . The system of  claim 1  further wherein the trained model, during a training phase, is configured to use machine learning for fraud training in order to identify inconsistent dates of service, duplicate billing entries, incorrect service codes, incorrect product codes, and unbundled charges. 
     
     
         15 . The system of  claim 1  further wherein the trained model, during a training phase, is configured to use machine learning for abuse training in order to identify excess charges, unnecessary products, and unnecessary services. 
     
     
         16 . The system of  claim 1  wherein the trained model, during a training phase, is configured to use machine learning to identify care gaps. 
     
     
         17 . The system of  claim 1  wherein the payment system is configured to generate a shopping cart ID associated with a shopping cart of an online retailer. 
     
     
         18 . A system for bill settlement comprising:
 a payment system configured to create a funding campaign;   the payment system having a distribution mechanism that is configured to transmit the funding campaign to a social network, wherein the payment system is configured to receive a commitment by a benefactor to provide a payment to a service provider and to receive a matching commitment by a sponsor that matches the commitment of the benefactor; and   the payment system having a payment mechanism configured to collect a payment from the benefactor and the sponsor based on the commitment and the matching commitment.   
     
     
         19 . The system of  claim 18  wherein the estimate of expense from a bill provider is converted into an invoice that includes amount owed to the service provider. 
     
     
         20 . The system of  claim 18  wherein unallocated excess of the payment received is used for payment of a future invoice.